8 papers
SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment
Tianyu Dong, Yangyang Liu, Jiang Zhou +9
Sparse Mixture-of-Experts (MoE) architectures have emerged as an increasingly influential paradigm as they offer a strategic balance between parameter scalability and computational…
Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEs
Bo Li, Tianyu Dong, Shaolin Zhu +1
Large Language Models (LLMs) have shown great promise in multilingual machine translation (MT), even with limited bilingual supervision. However, fine-tuning LLMs with parallel cor…
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan
Lei Yang, Leiyu Pan, Bojian Xiong +14
Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, yet their performance remains heavily biased toward high-res…
MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation
Bo Li, Ningyuan Deng, Tianyu Dong +3
Multimodal large language models (MLLMs) have shown impressive capabilities, yet they often struggle to effectively capture the fine-grained textual information within images cruci…
Incentivizing Parametric Knowledge via Reinforcement Learning with Verifiable Rewards for Cross-Cultural Entity Translation
Jiang Zhou, Xiaohu Zhao, Xinwei Wu +8
Cross-cultural entity translation remains challenging for large language models (LLMs) as literal or phonetic renderings are usually yielded instead of culturally appropriate trans…
DEP: A Decentralized Large Language Model Evaluation Protocol
Jianxiang Peng, Junhao Li, Hongxiang Wang +15
With the rapid development of Large Language Models (LLMs), a large number of benchmarks have been proposed. However, most benchmarks lack unified evaluation standard and require t…